Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting

Sarshar, Javad; Moosapour, Seyyed Sajjad; Joorabian, Mahmood

doi:10.1016/j.energy.2017.07.138

Cited by 94 publications

(56 citation statements)

References 33 publications

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“…Various optimization formulations have been proposed for CEM of MG [4]. These formulations are often aimed at minimizing operating costs [5][6][7][8][9][10][11][12][13] or at minimizing both the operating cost and emissions [14][15][16][17][18]. Sometimes objectives such as load curtailment index [19], voltage deviation [20], power losses [21], fuel consumption [22], and grid power profile fluctuations [23] are also considered as the objective function of MGEM problem.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Murty

Kumar

2020

Prot Control Mod Power Syst

229

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Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixedinteger linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it's impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO 2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it's effectiveness.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Murty

Kumar

2020

Prot Control Mod Power Syst

229

View full text Add to dashboard Cite

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“…Different optimization techniques have been used to solve the MGEM problem. These techniques includes robust optimization, evolutionary approach, linear programming, nonlinear programming, dynamic programming, stochastic programming, multi‐period imperialist competition, Lyapunov optimization, multi‐objective cross entropy, distributed algorithm, nondominated sorting genetic algorithm (GA), Particle Swarm Optimization (PSO), model predictive control, heuristic approach, fuzzy logic, multistep hierarchical, chance constrained programming, artificial intelligence, tabu search, graph theory, SOC‐based control strategy, MATPOWER, GA, flexible time frame, column and constraint generation algorithm, chaotic group search optimizer, Whale Optimization Algorithm (WOA), water cycle algorithm (WCA), Moth‐Flame Optimizer (MFO), and hybrid Particle Swarm‐Gravitational Search Algorithm (PSO‐GSA) . MGEM problem has been studied in conjunction with demand response (DR) program .…”

Section: Introductionmentioning

confidence: 99%

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

Murty

Kumar

2020

Int Trans Electr Energ Syst

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Summary Integration of renewable energy sources provides energy security, substantial cost savings, and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management (MGEM) is a challenging task for microgrid operator (MGO) for optimal energy utilization with penetration of renewable energy sources, energy storage devices, and demand response. In this paper, optimal energy dispatch strategy is established for grid‐connected and stand‐alone microgrids integrated with photovoltaic, wind turbine, fuel cell, microturbine, diesel generator, and battery storage system. Techno‐economic benefits are demonstrated for the hybrid power system considering uncertainty of load and generation. So far, MGEM problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and emission‐related objectives need to be addressed for effective MGEM. In this paper, MGEM is formulated as mixed‐integer linear programming, and a new multi‐objective solution is proposed for MGEM. Demand response is also included in the optimization problem to demonstrate its impact on optimal energy dispatch and techno‐commercial benefits. Simulation results are obtained for the optimal capacity of each component, charging/discharging scheduling, state of charge, power import from main grid, net present cost, cost of energy, initial cost, operational cost, fuel cost, and cost of emissions taking care of seasonal load variation throughout the year. The results show that CO2 emission in stand‐alone microgrid is reduced by 41.74% compared with traditional system with diesel generator. Simulation results obtained with the proposed method has been compared with various evolutionary algorithms to verify its effectiveness.

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“…Artificial neural network (ANN) has been widely applied as an intelligent model. Based on ANN, some papers have made predictions for wind power [22], wind speed [23], district-level electricity demand [24] and the water-alternating-CO 2 process [25]. Alireza Taheri-Rad et al simulated the energy flows for the production of various paddy rice cultivars [26]; Nadya et al simulated the relationship between spectral profiles and hardness values [27]; Raul et al modelled the electric arc furnace [28].…”

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confidence: 99%

Assessment Framework of Provincial Carbon Emission Peak Prediction in China: An Empirical Analysis of Hebei Province

Li¹,

Du²

2019

Pol. J. Environ. Stud.

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Greenhouse gas emission reduction has become the focus of today's research. In December 2009 the Chinese government proposed targets aimed at controlling greenhouse gas emissions. More specifically, it was decided that CO 2 emissions per unit gross domestic product (GDP) in China should be cut by 40-45% in 2020 compared to 2005. Then China and the United States jointly issued the "China-U.S. Joint Presidential Statement on Climate Change" in November 2014 [1], and China claimed to peak CO 2 emissions around 2030 and would endeavor to reach the carbon emission peak as soon as possible. Besides, China also made climate change action included in the "13 th Five-Year" Development Planning [2]. Since then, whether or not the target can be accomplished has become the focus of discussion. Hebei, as a large economic and energy consumption province, is located in the Bohai Sea Economic Zone and the Beijing Tianjin Hebei Economic Circle, with rich fossil energy and oil field resources [3]. Since the

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Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting

Cited by 94 publications

References 33 publications

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

Assessment Framework of Provincial Carbon Emission Peak Prediction in China: An Empirical Analysis of Hebei Province

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